Adapting CRISPDM for Social Sciences

The growth of available data in the social sciences led to
numerous knowledge discovery projects being launched over the
years. Even if the volume and the speed of data are increasing, in
social sciences data has an important limitation in terms of
methodological process that drives the conceptual and analytical
questions posed to the data. Social sciences domain experiences
several challenges in their desire of extracting useful and implicit
knowledge due to its inherent complexity and unique
characteristics, as well as the lack of standards for data mining
projects. The aim of this research is to bring Cross-Industry
Standard Process for Data Mining (CRISP-DM) methodology as a
standardization in analyzing large volumes of unstructured data to
generate analytical insights for wellbeing and social sciences topics
in general. Also, taking into consideration that for a data scientist,
the most time-consuming activity is data preparation step, we are
trying to make more efficient this process using a clear
methodology and tasks. Conclusion is that using a strong
methodology with well-defined steps in research can increase
productivity in terms of time and enhance the quality of the
research.